Abstract For well over a century, the journal Science Education has been publishing articles about the teaching and learning of science. These articles represent more than just a repository of past work: they have the potential to offer insights into both the history of science education as well as well as the dynamics of field‐specific change. It can be difficult, however, for educators, researchers, reformers, and policymakers to grasp the nuances of over 100 years of scholarship given the overwhelming amount of textual material. To address this problem, we have used latent Dirichlet allocation, an automated machine‐learning algorithm from the field of natural language processing, to perform an automated literature review and classification of the corpus of work in Science Education . Using this technique, we have classified research in the journal into 21 distinct topics, falling into three thematic groups: science content topics, teaching‐focused topics, and student‐focused topics. We have also quantified the rise and fall of these topics and groups over time, and used them to begin to extract insight into the development of the field, including the effects of national policy changes on topics of interest to the research community, the interrelationships between different research topics, and the effects of intellectual cross‐pollination. Based on this analysis, we argue that this technique shows great promise for even larger‐scale analyses of educational literature and other textual data.
Odden et al. (Mon,) studied this question.